TY - JOUR
T1 - Local binary patterns on triangular meshes
T2 - Concept and applications
AU - Werghi, Naoufel
AU - Tortorici, Claudio
AU - Berretti, Stefano
AU - Bimbo, Alberto Del
N1 - Funding Information:
Preliminary ideas and experiments of this research appeared in [44] . Part of this work is supported by a National Research Foundation Grant UIRCA 2013-24877, and C4 Advanced Solutions, UAE.
Publisher Copyright:
© 2015 Elsevier Inc. All rights reserved.
PY - 2015/8/22
Y1 - 2015/8/22
N2 - In this paper, we introduce an original framework for computing local binary like-patterns on 2D mesh manifolds (i.e., surfaces in the 3D space). This framework, dubbed mesh-LBP, preservers the simplicity and the adaptability of the 2D LBP and has the capacity of handling both open and close mesh surfaces without requiring normalization as compared to its 2D counterpart. We describe the foundations and the construction of mesh-LBP and showcase the different LBP patterns that can be generated on the mesh. In the experimentation, we provide evidence of the uniform patterns in the mesh-LBP, the repeatability of its descriptors, and its robustness to moderate shape deformations. Then, we show how the mesh-LBP descriptors can be adapted to a number of surface local and global analysis including 3D texture classification and retrieval, and 3D face matching. We also compare the performance of the mesh-LBP descriptors with a bunch of state of the art surface descriptors.
AB - In this paper, we introduce an original framework for computing local binary like-patterns on 2D mesh manifolds (i.e., surfaces in the 3D space). This framework, dubbed mesh-LBP, preservers the simplicity and the adaptability of the 2D LBP and has the capacity of handling both open and close mesh surfaces without requiring normalization as compared to its 2D counterpart. We describe the foundations and the construction of mesh-LBP and showcase the different LBP patterns that can be generated on the mesh. In the experimentation, we provide evidence of the uniform patterns in the mesh-LBP, the repeatability of its descriptors, and its robustness to moderate shape deformations. Then, we show how the mesh-LBP descriptors can be adapted to a number of surface local and global analysis including 3D texture classification and retrieval, and 3D face matching. We also compare the performance of the mesh-LBP descriptors with a bunch of state of the art surface descriptors.
KW - 3D face matching
KW - 3D local binary patterns
KW - 3D texture classification
KW - 3D texture retrieval
KW - Mesh manifold
KW - Ordered ring facets
UR - http://www.scopus.com/inward/record.url?scp=84939803816&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2015.03.016
DO - 10.1016/j.cviu.2015.03.016
M3 - Article
AN - SCOPUS:84939803816
SN - 1077-3142
VL - 139
SP - 161
EP - 177
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
ER -